'''simple docstring'''




import os

import jsonlines
import numpy as np
from tqdm import tqdm


__UpperCAmelCase					=2_0_4_8
__UpperCAmelCase					=4_0_9_6
__UpperCAmelCase					=4_2
__UpperCAmelCase					=os.environ.pop("PROCESS_TRAIN", "false")
__UpperCAmelCase					={"null": 0, "short": 1, "long": 2, "yes": 3, "no": 4}






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       Optional[Any]:
    def choose_first(UpperCamelCase__							,    UpperCamelCase__=False							):
        assert isinstance(UpperCamelCase__							,    UpperCamelCase__							)
        if len(UpperCamelCase__							) == 1:
            __lowerCamelCase            =    answer[0]
            return {k: [answer[k]] for k in answer} if is_long_answer else answer
        for a in answer:
            if is_long_answer:
                __lowerCamelCase            =    {k: [a[k]] for k in a}
            if len(a['''start_token''']							) > 0:
                break
        return a

    __lowerCamelCase            =    {'''id''': example['''id''']}
    __lowerCamelCase            =    example['''annotations''']
    __lowerCamelCase            =    annotation['''yes_no_answer''']
    if 0 in yes_no_answer or 1 in yes_no_answer:
        __lowerCamelCase            =    ['''yes'''] if 1 in yes_no_answer else ['''no''']
        __lowerCamelCase            =    __lowerCamelCase            =    []
        __lowerCamelCase            =    __lowerCamelCase            =    []
        __lowerCamelCase            =    ['''<cls>''']
    else:
        __lowerCamelCase            =    ['''short''']
        __lowerCamelCase            =    choose_first(annotation['''short_answers''']							)
        if len(out['''start_token''']							) == 0:
            # answer will be long if short is not available
            __lowerCamelCase            =    ['''long''']
            __lowerCamelCase            =    choose_first(annotation['''long_answer''']							,    is_long_answer=UpperCamelCase__							)
            __lowerCamelCase            =    []
        answer.update(UpperCamelCase__							)

    # disregard some samples
    if len(answer['''start_token''']							) > 1 or answer["start_token"] == answer["end_token"]:
        __lowerCamelCase            =    True
    else:
        __lowerCamelCase            =    False

    __lowerCamelCase            =    ['''start_token''', '''end_token''', '''start_byte''', '''end_byte''', '''text''']
    if not all(isinstance(answer[k]							,    UpperCamelCase__							) for k in cols							):
        raise ValueError('''Issue in ID'''							,    example['''id''']							)

    return answer






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__=False							)      ->       List[Any]:
    __lowerCamelCase            =    _get_single_answer(UpperCamelCase__							)
    # bytes are of no use
    del answer["start_byte"]
    del answer["end_byte"]

    # handle yes_no answers explicitly
    if answer["category"][0] in ["yes", "no"]:  # category is list with one element
        __lowerCamelCase            =    example['''document''']['''tokens''']
        __lowerCamelCase            =    []
        for i in range(len(doc['''token''']							)							):
            if not doc["is_html"][i]:
                context.append(doc['''token'''][i]							)
        return {
            "context": " ".join(UpperCamelCase__							),
            "answer": {
                "start_token": -1_00,  # ignore index in cross-entropy
                "end_token": -1_00,  # ignore index in cross-entropy
                "category": answer["category"],
                "span": answer["category"],  # extra
            },
        }

    # later, help in removing all no answers
    if answer["start_token"] == [-1]:
        return {
            "context": "None",
            "answer": {
                "start_token": -1,
                "end_token": -1,
                "category": "null",
                "span": "None",  # extra
            },
        }

    # handling normal samples

    __lowerCamelCase            =    ['''start_token''', '''end_token''']
    answer.update({k: answer[k][0] if len(answer[k]							) > 0 else answer[k] for k in cols}							)  # e.g. [10] == 10

    __lowerCamelCase            =    example['''document''']['''tokens''']
    __lowerCamelCase            =    answer['''start_token''']
    __lowerCamelCase            =    answer['''end_token''']

    __lowerCamelCase            =    []
    for i in range(len(doc['''token''']							)							):
        if not doc["is_html"][i]:
            context.append(doc['''token'''][i]							)
        else:
            if answer["start_token"] > i:
                start_token -= 1
            if answer["end_token"] > i:
                end_token -= 1
    __lowerCamelCase            =    ''' '''.join(context[start_token:end_token]							)

    # checking above code
    if assertion:
        __lowerCamelCase            =    doc['''is_html'''][answer['''start_token'''] : answer['''end_token''']]
        __lowerCamelCase            =    doc['''token'''][answer['''start_token'''] : answer['''end_token''']]
        __lowerCamelCase            =    ''' '''.join([old[i] for i in range(len(UpperCamelCase__							)							) if not is_html[i]]							)
        if new != old:
            print('''ID:'''							,    example['''id''']							)
            print('''New:'''							,    UpperCamelCase__							,    end='''\n'''							)
            print('''Old:'''							,    UpperCamelCase__							,    end='''\n\n'''							)

    return {
        "context": " ".join(UpperCamelCase__							),
        "answer": {
            "start_token": start_token,
            "end_token": end_token - 1,  # this makes it inclusive
            "category": answer["category"],  # either long or short
            "span": new,  # extra
        },
    }






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__=20_48							,    UpperCamelCase__=40_96							,    UpperCamelCase__=True							)      ->       List[Any]:
    # overlap will be of doc_stride - q_len

    __lowerCamelCase            =    get_context_and_ans(UpperCamelCase__							,    assertion=UpperCamelCase__							)
    __lowerCamelCase            =    out['''answer''']

    # later, removing these samples
    if answer["start_token"] == -1:
        return {
            "example_id": example["id"],
            "input_ids": [[-1]],
            "labels": {
                "start_token": [-1],
                "end_token": [-1],
                "category": ["null"],
            },
        }

    __lowerCamelCase            =    tokenizer(example['''question''']['''text''']							,    out['''context''']							).input_ids
    __lowerCamelCase            =    input_ids.index(tokenizer.sep_token_id							) + 1

    # return yes/no
    if answer["category"][0] in ["yes", "no"]:  # category is list with one element
        __lowerCamelCase            =    []
        __lowerCamelCase            =    []
        __lowerCamelCase            =    input_ids[:q_len]
        __lowerCamelCase            =    range(UpperCamelCase__							,    len(UpperCamelCase__							)							,    max_length - doc_stride							)
        for i in doc_start_indices:
            __lowerCamelCase            =    i + max_length - q_len
            __lowerCamelCase            =    input_ids[i:end_index]
            inputs.append(q_indices + slice							)
            category.append(answer['''category'''][0]							)
            if slice[-1] == tokenizer.sep_token_id:
                break

        return {
            "example_id": example["id"],
            "input_ids": inputs,
            "labels": {
                "start_token": [-1_00] * len(UpperCamelCase__							),
                "end_token": [-1_00] * len(UpperCamelCase__							),
                "category": category,
            },
        }

    __lowerCamelCase            =    out['''context'''].split()
    __lowerCamelCase            =    splitted_context[answer['''end_token''']]
    __lowerCamelCase            =    len(
        tokenizer(
            ''' '''.join(splitted_context[: answer['''start_token''']]							)							,    add_special_tokens=UpperCamelCase__							,    ).input_ids							)
    __lowerCamelCase            =    len(
        tokenizer(''' '''.join(splitted_context[: answer['''end_token''']]							)							,    add_special_tokens=UpperCamelCase__							).input_ids							)

    answer["start_token"] += q_len
    answer["end_token"] += q_len

    # fixing end token
    __lowerCamelCase            =    len(tokenizer(UpperCamelCase__							,    add_special_tokens=UpperCamelCase__							).input_ids							)
    if num_sub_tokens > 1:
        answer["end_token"] += num_sub_tokens - 1

    __lowerCamelCase            =    input_ids[answer['''start_token'''] : answer['''end_token'''] + 1]  # right & left are inclusive
    __lowerCamelCase            =    answer['''start_token''']
    __lowerCamelCase            =    answer['''end_token''']

    if assertion:
        __lowerCamelCase            =    tokenizer.decode(UpperCamelCase__							)
        if answer["span"] != new:
            print('''ISSUE IN TOKENIZATION'''							)
            print('''OLD:'''							,    answer['''span''']							)
            print('''NEW:'''							,    UpperCamelCase__							,    end='''\n\n'''							)

    if len(UpperCamelCase__							) <= max_length:
        return {
            "example_id": example["id"],
            "input_ids": [input_ids],
            "labels": {
                "start_token": [answer["start_token"]],
                "end_token": [answer["end_token"]],
                "category": answer["category"],
            },
        }

    __lowerCamelCase            =    input_ids[:q_len]
    __lowerCamelCase            =    range(UpperCamelCase__							,    len(UpperCamelCase__							)							,    max_length - doc_stride							)

    __lowerCamelCase            =    []
    __lowerCamelCase            =    []
    __lowerCamelCase            =    []
    __lowerCamelCase            =    []  # null, yes, no, long, short
    for i in doc_start_indices:
        __lowerCamelCase            =    i + max_length - q_len
        __lowerCamelCase            =    input_ids[i:end_index]
        inputs.append(q_indices + slice							)
        assert len(inputs[-1]							) <= max_length, "Issue in truncating length"

        if start_token >= i and end_token <= end_index - 1:
            __lowerCamelCase            =    start_token - i + q_len
            __lowerCamelCase            =    end_token - i + q_len
            answers_category.append(answer['''category'''][0]							)  # ["short"] -> "short"
        else:
            __lowerCamelCase            =    -1_00
            __lowerCamelCase            =    -1_00
            answers_category.append('''null'''							)
        __lowerCamelCase            =    inputs[-1][start_token : end_token + 1]

        answers_start_token.append(UpperCamelCase__							)
        answers_end_token.append(UpperCamelCase__							)
        if assertion:
            if new != old and new != [tokenizer.cls_token_id]:
                print('''ISSUE in strided for ID:'''							,    example['''id''']							)
                print('''New:'''							,    tokenizer.decode(UpperCamelCase__							)							)
                print('''Old:'''							,    tokenizer.decode(UpperCamelCase__							)							,    end='''\n\n'''							)
        if slice[-1] == tokenizer.sep_token_id:
            break

    return {
        "example_id": example["id"],
        "input_ids": inputs,
        "labels": {
            "start_token": answers_start_token,
            "end_token": answers_end_token,
            "category": answers_category,
        },
    }






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__=20_48							,    UpperCamelCase__=40_96							,    UpperCamelCase__=False							)      ->       List[Any]:
    __lowerCamelCase            =    get_strided_contexts_and_ans(
        UpperCamelCase__							,    UpperCamelCase__							,    doc_stride=UpperCamelCase__							,    max_length=UpperCamelCase__							,    assertion=UpperCamelCase__							,    )

    return example






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							)      ->       Tuple:
    with jsonlines.open(UpperCamelCase__							,    '''a'''							) as writer:
        for example in tqdm(UpperCamelCase__							,    total=len(UpperCamelCase__							)							,    desc='''Saving samples ... '''							):
            __lowerCamelCase            =    example['''labels''']
            for ids, start, end, cat in zip(
                example['''input_ids''']							,    labels['''start_token''']							,    labels['''end_token''']							,    labels['''category''']							,    ):
                if start == -1 and end == -1:
                    continue  # leave waste samples with no answer
                if cat == "null" and np.random.rand() < 0.6:
                    continue  # removing 50 % samples
                writer.write(
                    {
                        '''input_ids''': ids,
                        '''start_token''': start,
                        '''end_token''': end,
                        '''category''': CATEGORY_MAPPING[cat],
                    }							)


if __name__ == "__main__":
  from datasets import load_dataset

  from transformers import BigBirdTokenizer

  __UpperCAmelCase					=load_dataset("natural_questions")
  __UpperCAmelCase					=BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base")

  __UpperCAmelCase					=data["train" if PROCESS_TRAIN == "true" else "validation"]

  __UpperCAmelCase					={
      "tokenizer": tokenizer,
      "doc_stride": DOC_STRIDE,
      "max_length": MAX_LENGTH,
      "assertion": False,
  }
  __UpperCAmelCase					=data.map(prepare_inputs, fn_kwargs=fn_kwargs)
  __UpperCAmelCase					=data.remove_columns(["annotations", "document", "id", "question"])
  print(data)

  np.random.seed(SEED)
  __UpperCAmelCase					="nq-training.jsonl" if PROCESS_TRAIN == "true" else "nq-validation.jsonl"
  save_to_disk(data, file_name=cache_file_name)

'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCAmelCase					={
    "configuration_mobilebert": [
        "MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileBertConfig",
        "MobileBertOnnxConfig",
    ],
    "tokenization_mobilebert": ["MobileBertTokenizer"],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase					=["MobileBertTokenizerFast"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase					=[
      "MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "MobileBertForMaskedLM",
      "MobileBertForMultipleChoice",
      "MobileBertForNextSentencePrediction",
      "MobileBertForPreTraining",
      "MobileBertForQuestionAnswering",
      "MobileBertForSequenceClassification",
      "MobileBertForTokenClassification",
      "MobileBertLayer",
      "MobileBertModel",
      "MobileBertPreTrainedModel",
      "load_tf_weights_in_mobilebert",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase					=[
      "TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFMobileBertForMaskedLM",
      "TFMobileBertForMultipleChoice",
      "TFMobileBertForNextSentencePrediction",
      "TFMobileBertForPreTraining",
      "TFMobileBertForQuestionAnswering",
      "TFMobileBertForSequenceClassification",
      "TFMobileBertForTokenClassification",
      "TFMobileBertMainLayer",
      "TFMobileBertModel",
      "TFMobileBertPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_mobilebert import (
      MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      MobileBertConfig,
      MobileBertOnnxConfig,
  )
  from .tokenization_mobilebert import MobileBertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_mobilebert_fast import MobileBertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mobilebert import (
        MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        MobileBertForMaskedLM,
        MobileBertForMultipleChoice,
        MobileBertForNextSentencePrediction,
        MobileBertForPreTraining,
        MobileBertForQuestionAnswering,
        MobileBertForSequenceClassification,
        MobileBertForTokenClassification,
        MobileBertLayer,
        MobileBertModel,
        MobileBertPreTrainedModel,
        load_tf_weights_in_mobilebert,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_mobilebert import (
        TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFMobileBertForMaskedLM,
        TFMobileBertForMultipleChoice,
        TFMobileBertForNextSentencePrediction,
        TFMobileBertForPreTraining,
        TFMobileBertForQuestionAnswering,
        TFMobileBertForSequenceClassification,
        TFMobileBertForTokenClassification,
        TFMobileBertMainLayer,
        TFMobileBertModel,
        TFMobileBertPreTrainedModel,
    )

else:
  import sys

  __UpperCAmelCase					=_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
